Image Segmentation
Transformers
Safetensors
background-removal
image-matting
BiRefNet
transparency
camouflage
text-preservation
illustration
rgba
custom_code
Instructions to use egeorcun/lucida with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egeorcun/lucida with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="egeorcun/lucida", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("egeorcun/lucida", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27984908c598e8fb75fde49d6d06846d57a3cf81a3616c1d4ed4dd4cd14c9ab5
- Size of remote file:
- 885 MB
- SHA256:
- 2f1aa6913426537d4b93dd5f7138ae5c6664e99abda98c95f3d5b9101283e7d5
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